S&P-500 vs. Nasdaq-100 price movement prediction with LSTM for different daily periods

Abstract

This paper explores the efficiency of LSTM neural networks in predicting price movements for the two major U.S. stock indices: the S&P-500 and the Nasdaq-100 index. We consider three distinct daily periods: “overnight” (Close-to-Open), “daytime” (Open-to-Close) and “24-hour” (Close-to-Close) trading sessions. Using historical pricing data for these indices since 2000, this study shows how well the standard LSTM model captures price movement patterns to improve short-term trading strategies. The findings reveal that, for the S&P-500, a one-year training with 24-hour periods delivers a 14.5% more return over the Buy-and-Hold strategy. Moreover, combining “overnight” and “daytime” strategies delivers more than 40% return compared to passive index investing. By contrast, for the Nasdaq-100, a shorter training period of three months for “24-hour” periods delivers 90% more return than passive index investing. These results suggest that LSTM effectively learns the unique market dynamics associated with each index and different time periods, offering further insights into how deep learning can enhance financial forecasting and trading opportunities.